Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to p...Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to participate in this online questionnaire survey.The survey tools included a general information questionnaire and a self-made NMOSD symptoms scale,which included the prevalence,severity,and distress of 29 symptoms.Cluster analysis was used to identify symptom clusters,and network analysis was used to analyze the symptom network and node characteristics and central indicators including strength centrality(r_(s)),closeness centrality(r_(c))and betweeness centrality(r_(b))were used to identify core symptoms and symptom clusters.Results The most common symptom was pain(65.7%),followed by paraesthesia(65.0%),fatigue(65.0%),easy awakening(63.6%).Regarding the burden level of symptoms,pain was the most burdensome symptom,followed by paraesthesia,easy awakening,fatigue,and difficulty falling asleep.Six clusters were identified:somatosensory,motor,visual,and memory symptom clusters,bladder and rectum symptom clusters,sleep symptoms clusters,and neuropsychological symptom clusters.Fatigue(r_(s)=12.39,r_(b)=68.00,r_(c)=0.02)was the most central and prominent bridge symptom,and motor symptom cluster(r_(s)=2.68,r_(c)=0.10)was the most central symptom cluster among the six clusters.Conclusions Our study demonstrated the necessity of symptom management targeting fatigue,pain,and motor symptom cluster in patients with NMOSD.展开更多
Traditional Chinese medicine(TCM)has played a significant role in the prevention and treatment of chronic heart failure(CHF).To study TCM diagnosis of CHF,a total of 278 Chinese clinical research articles on the study...Traditional Chinese medicine(TCM)has played a significant role in the prevention and treatment of chronic heart failure(CHF).To study TCM diagnosis of CHF,a total of 278 Chinese clinical research articles on the study of CHF syndromes in recent 40 years retrieved from Web of Science,Scopus,Pub Med,Embase,CNKI,Wanfang Data,Cq VIP,and Sino Med.According to cumulative frequency analysis,network analysis,and hierarchical cluster analysis,the study found the distribution of CHF syndromes was syndrome of qi deficiency with blood stasis,syndrome of qi and yin deficiency,syndrome of yang deficiency with water flooding,syndrome of heart blood stasis obstruction,syndrome of turbid phlegm,and syndrome of collapse due to primordial yang deficiency.The syndrome elements on location of illness were heart,kidney,lung,and spleen.The syndrome elements on nature of illness were qi deficiency,blood stasis,yang deficiency,yin deficiency,water retention,and turbid phlegm.These findings can provide reference to the research on diagnosis and treatment of CHF,and contribute to the study on syndrome standardization and objective research of TCM diagnosis.展开更多
Purpose:This study analyzes the profiles of elite Brazilian researchers,recognized through the prestigious CNPq productivity scholarships.By identifying distinct researcher clusters,the study sheds light on different ...Purpose:This study analyzes the profiles of elite Brazilian researchers,recognized through the prestigious CNPq productivity scholarships.By identifying distinct researcher clusters,the study sheds light on different academic strategies,levels of productivity,and academic contributions within the Brazilian higher education system.Design/methodology/approach:The research analyzes a comprehensive dataset of 14,003 researchers,employing principal component analysis(PCA)followed by cluster analysis to group researchers based on their academic attributes.The clusters reflect diverse aspects of research productivity,graduate supervisions,and publication patterns.Findings:The analysis reveals the existence of three distinct researcher profiles(the Advanced Supervisors,the Book Publishers/Organizers,and the Generalists).The study also highlights the characteristics of highcaliber scientists,representing the upper echelon of Brazilian researchers in terms of productivity and impact.Research limitations:Although the study provides a robust analysis of the Brazilian system,the results reflect specific characteristics of the Brazilian academic context.Furthermore,the analysis was restricted to normalized annual data,which may overlook temporal variations in researcher productivity.Pratical implications:The findings provide valuable insights for policy makers,funding agencies(such as CNPq),and university administrators who aim to develop tailored support programs for different researcher profiles.Originality/value:The cluster-based profiling offers a novel perspective on how different academic trajectories coexist within a national science system,offering lessons for other emerging economies.展开更多
With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing hig...With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods,this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification.To resolve the scalability issue of coupled transmission and distribution power systems,clustering is first carried out based on the distribution system states.As the data and models of the transmission system and distribution systems are not shared.For the transmission system,equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system.Further,the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models.Then,a parameter identification method is proposed to obtain the parameters of the equivalent load model.Finally,a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model.The results of the numerical analysis show that compared with the traditional master-slave splitting method,the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy.展开更多
Cycle slip detection and repair is one of the key technologies for GNSS high-precision positioning.We introduce an enhanced methodology for detecting and repairing BDS four-frequency cycle slips,utilizing fuzzy cluste...Cycle slip detection and repair is one of the key technologies for GNSS high-precision positioning.We introduce an enhanced methodology for detecting and repairing BDS four-frequency cycle slips,utilizing fuzzy clustering analysis.Firstly,based on fuzzy clustering analysis,the optimal combinations for the BDS four-frequency,including extra-wide lane(EWL),wide lane(WL),and narrow lane(NL),were selected.Secondly,the feasibility of this method was verified using actual static and dynamic observation data,and different types of cycle slips were simulated for further validation.Meanwhile,the proposed method was compared with the classical Turbo-Edit method through experiments.Finally,cycle slips were repaired using the least squares method.According to the experimental results,the optimal geometry-free phase combinations(-2,2,1,-1),(1,-1,1,-1),(3,2,-2,-3),and the pseudo-range phase combination(-1,1,1,-1),selected based on fuzzy clustering analysis,were used for cycle slip detection.The proposed method accurately detected small,large,and specific cycle slips simulated in the actual data.Compared with the Turbo-Edit method,the proposed methodwas able to detect specific cycle slips that Turbo-Edit could not.It is worth noting that during the repair process,the coefficients of the combined observation values are integers,preserving the integer cycle characteristic of the observation values,which allows cycle slips to be fixed directly,eliminating the need for complex searching procedures.Consequently,by enhancing the precision and reliability of the detection of BDS four-frequency cycle slips,our proposed method provides the support for the high-precision localization of BDS multi-frequency observations.展开更多
The issue of strong noise has increasingly become a bottleneck restricting the precision and application space of electromagnetic exploration methods.Noise suppression and extraction of effective electromagnetic respo...The issue of strong noise has increasingly become a bottleneck restricting the precision and application space of electromagnetic exploration methods.Noise suppression and extraction of effective electromagnetic response information under a strong noise background is a crucial scientific task to be addressed.To solve the noise suppression problem of the controlled-source electromagnetic method in strong interference areas,we propose an approach based on complex-plane 2D k-means clustering for data processing.Based on the stability of the controlled-source signal response,clustering analysis is applied to classify the spectra of different sources and noises in multiple time segments.By identifying the power spectra with controlled-source characteristics,it helps to improve the quality of the controlled-source response extraction.This paper presents the principle and workflow of the proposed algorithm,and demonstrates feasibility and effectiveness of the new algorithm through synthetic and real data examples.The results show that,compared with the conventional Robust denoising method,the clustering algorithm has a stronger suppression effect on common noise,can identify high-quality signals,and improve the preprocessing data quality of the controlledsource electromagnetic method.展开更多
Side-channel analysis(SCA)has emerged as a research hotspot in the field of cryptanalysis.Among various approaches,unsupervised deep learning-based methods demonstrate powerful information extraction capabilities with...Side-channel analysis(SCA)has emerged as a research hotspot in the field of cryptanalysis.Among various approaches,unsupervised deep learning-based methods demonstrate powerful information extraction capabilities without requiring labeled data.However,existing unsupervised methods,particularly those represented by differential deep learning analysis(DDLA)and its improved variants,while overcoming the dependency on labeled data inherent in template analysis,still suffer from high time complexity and training costs when handling key byte difference comparisons.To address this issue,this paper introduces invariant information clustering(IIC)into SCA for the first time,and thus proposes a novel unsupervised learning-based SCA method,named IIC-SCA.By leveraging mutual information maximization techniques for automatic feature extraction of power leakage data,our approach achieves key recovery through a single training session,eliminating the prohibitive computational overhead of traditional methods that require separate training for all possible key bytes.Experimental results on the ASCAD dataset demonstrate successful key extraction using only 50000 training traces and 2000 attack traces.Furthermore,compared with DDLA,the proposed method reduces training time by approximately 93.40%and memory consumption by about 6.15%,significantly decreasing the temporal and resource costs of unsupervised SCA.This breakthrough provides new insights for developing low-cost,high-efficiency cryptographic attack methodologies.展开更多
Objective:This study aims to investigate the patterns of symptom occurrence in patients experiencing acute exacerbations of chronic obstructive pulmonary disease(AECOPD).It will explore the composition of symptom clus...Objective:This study aims to investigate the patterns of symptom occurrence in patients experiencing acute exacerbations of chronic obstructive pulmonary disease(AECOPD).It will explore the composition of symptom clusters and analyze the correlation between these clusters and health-related quality of life(HRQoL).Methods:A total of 207 patients with AE-COPD were surveyed from a tertiary grade A hospital.Data collection was conducted using three validated instruments:the Basic Information Questionnaire(BIQ),Disease Symptom Survey Questionnaire(MSAS),and Quality of Life Questionnaire(CAT).Statistical software SPSS 22.0 was used to analyze the correlation between symptom clusters and quality of life.Results:Exploratory factor analysis showed that five major symptom clusters existed in the patients,including the psycho-emotional symptom cluster,the sleep-related symptom cluster,the other side effects symptom cluster,the energy deficiency symptom cluster and the cough-loss of appetite symptom cluster,and the severity of the symptom clusters showed a significant negative correlation with the quality of life of the patients(P<0.05).Conclusion:Strengthening the comprehensive management of symptom clusters in patients with AE-COPD can help to effectively reduce the symptom burden of patients,and then significantly improve their quality of life.展开更多
In recent years,with the rapid development of software systems,the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics.Defe...In recent years,with the rapid development of software systems,the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics.Defect prediction methods based on software metric elements highly rely on software metric data.However,redundant software metric data is not conducive to efficient defect prediction,posing severe challenges to current software defect prediction tasks.To address these issues,this paper focuses on the rational clustering of software metric data.Firstly,multiple software projects are evaluated to determine the preset number of clusters for software metrics,and various clustering methods are employed to cluster the metric elements.Subsequently,a co-occurrence matrix is designed to comprehensively quantify the number of times that metrics appear in the same category.Based on the comprehensive results,the software metric data are divided into two semantic views containing different metrics,thereby analyzing the semantic information behind the software metrics.On this basis,this paper also conducts an in-depth analysis of the impact of different semantic view of metrics on defect prediction results,as well as the performance of various classification models under these semantic views.Experiments show that the joint use of the two semantic views can significantly improve the performance of models in software defect prediction,providing a new understanding and approach at the semantic view level for defect prediction research based on software metrics.展开更多
Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully construct...Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully constructed by coordinatively assembling the semi-rigid multidentate ligand 5-(1-carboxyethoxy)isophthalic acid(H₃CIA)with the Nheterocyclic ligands 1,4-di(4H-1,2,4-triazol-4-yl)benzene(1,4-dtb)and 1,4-di(1H-imidazol-1-yl)benzene(1,4-dib),respectively,around Co^(2+)ions.Single-crystal X-ray diffraction analysis revealed that in both complexes HU23 and HU24,the CIA^(3-)anions adopt aκ^(7)-coordination mode,bridging six Co^(2+)ions via their five carboxylate oxygen atoms and one ether oxygen atom.This linkage forms tetranuclear[Co4(μ3-OH)2]^(6+)units.These Co-oxo cluster units were interconnected by CIA^(3-)anions to assemble into 2D kgd-type structures featuring a 3,6-connected topology.The 2D layers were further connected by 1,4-dtb and 1,4-dib,resulting in 3D pillar-layered frameworks for HU23 and HU24.Notably,despite the similar configurations of 1,4-dtb and 1,4-dib,differences in their coordination spatial orientations lead to topological divergence in the 3D frameworks of HU23 and HU24.Topological analysis indicates that the frameworks of HU23 and HU24 can be simplified into a 3,10-connected net(point symbol:(4^(10).6^(3).8^(2))(4^(3))_(2))and a 3,8-connected tfz-d net(point symbol:(4^(3))_(2)((4^(6).6^(18).8^(4)))),respectively.This structural differentiation confirms the precise regulatory role of ligands on the topology of metal-organic frameworks.Moreover,the ultraviolet-visible absorption spectra confirmed that HU23 and HU24 have strong absorption capabilities for ultraviolet and visible light.According to the Kubelka-Munk method,their bandwidths were 2.15 and 2.08 eV,respectively,which are consistent with those of typical semiconductor materials.Variable-temperature magnetic susceptibility measurements(2-300 K)revealed significant antiferromagnetic coupling in both complexes,with their effective magnetic moments decreasing markedly as the temperature lowered.CCDC:2457554,HU23;2457553,HU24.展开更多
Six new lanthanide complexes:[Ln(3,4-DEOBA)3(4,4'-DM-2,2'-bipy)]2·2C_(2)H_(5)OH,[Ln=Dy(1),Eu(2),Tb(3),Sm(4),Ho(5),Gd(6);3,4-DEOBA-=3,4-diethoxybenzoate,4,4'-DM-2,2'-bipy=4,4'-dimethyl-2,2'...Six new lanthanide complexes:[Ln(3,4-DEOBA)3(4,4'-DM-2,2'-bipy)]2·2C_(2)H_(5)OH,[Ln=Dy(1),Eu(2),Tb(3),Sm(4),Ho(5),Gd(6);3,4-DEOBA-=3,4-diethoxybenzoate,4,4'-DM-2,2'-bipy=4,4'-dimethyl-2,2'-bipyridine]were successfully synthesized by the volatilization of the solution at room temperature.The crystal structures of six complexes were determined by single-crystal X-ray diffraction technology.The results showed that the complexes all have a binuclear structure,and the structures contain free ethanol molecules.Moreover,the coordination number of the central metal of each structural unit is eight.Adjacent structural units interact with each other through hydrogen bonds and further expand to form 1D chain-like and 2D planar structures.After conducting a systematic study on the luminescence properties of complexes 1-4,their emission and excitation spectra were obtained.Experimental results indicated that the fluorescence lifetimes of complexes 2 and 3 were 0.807 and 0.845 ms,respectively.The emission spectral data of complexes 1-4 were imported into the CIE chromaticity coordinate system,and their corre sponding luminescent regions cover the yellow light,red light,green light,and orange-red light bands,respectively.Within the temperature range of 299.15-1300 K,the thermal decomposition processes of the six complexes were comprehensively analyzed by using TG-DSC/FTIR/MS technology.The hypothesis of the gradual loss of ligand groups during the decomposition process was verified by detecting the escaped gas,3D infrared spectroscopy,and ion fragment information detected by mass spectrometry.The specific decomposition path is as follows:firstly,free ethanol molecules and neutral ligands are removed,and finally,acidic ligands are released;the final product is the corresponding metal oxide.CCDC:2430420,1;2430422,2;2430419,3;2430424,4;2430421,5;2430423,6.展开更多
Hepatitis B virus remains a major cause of cirrhosis and hepatocellular carcinoma,with genetic polymorphisms and mutations influencing immune responses and disease progression.Nguyen et al present novel findings on sp...Hepatitis B virus remains a major cause of cirrhosis and hepatocellular carcinoma,with genetic polymorphisms and mutations influencing immune responses and disease progression.Nguyen et al present novel findings on specific human leukocyte antigen(HLA)alleles,including rs2856718 of HLA-DQ and rs3077 and rs9277535 of HLA-DP,which may predispose individuals to cirrhosis and liver cancer,based on multi-clustering analysis.Here,we discuss the feasibility of this approach and identify key areas for further investigation,aiming to offer insights for advancing clinical practice and research in liver disease and related cancers.展开更多
Objective Sepsis patients exhibit diverse immune states,making it crucial to identify subtypes with distinct inflammatory profiles through Th1/Th2 cytokine data for personalized treatment and improved prognosis.Method...Objective Sepsis patients exhibit diverse immune states,making it crucial to identify subtypes with distinct inflammatory profiles through Th1/Th2 cytokine data for personalized treatment and improved prognosis.Methods We retrieved data from sepsis patients who underwent Th1/Th2 cytokine testing in Nanfang Hospital,Southern Medical University from June 1,2020,to February 1,2022.An unsupervised K-means clustering method classified participants based on Th1/Th2 cytokine levels,with the primary outcome being the 7-day mortality rate post-ICU admission.Cox proportional hazards and Restricted Mean Survival Time(RMST)analyses were utilized to explore survival outcomes.Results A total of 321 sepsis patients were included.IL-6(HR 1.69,95%CI:1.22,2.34)and IL-10(HR 1.81,95%CI:1.37,2.40)emerged as independent predictors of 7-day mortality.Unsupervised K-means clustering revealed 3 inflammatory/immune subgroups:Cluster 1(n=166,low inflammatory response),Cluster 2(n=99,moderate inflammatory response with immune suppression),and Cluster 3(n=56,strong inflammatory and immune suppression).Compared to Cluster 1,Clusters 2 and 3 had higher 7-day mortality risks(14.4%vs 23.2%,HR=4.30,95%CI:1.51-12.26;14.4%vs 35.7%,HR=7.32,95%CI:2.57-20.79).Conclusion Septic patients in a protective immune response state(Cluster 1)exhibit better short-term prognoses,suggesting the importance of understanding inflammatory/immune states for precise treatment and improved outcomes.展开更多
Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macro...Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macrophages have been poorly understood and largely overlooked. However, a recent study reported that border-associated macrophages participate in stroke-induced inflammation, although many details and the underlying mechanisms remain unclear. In this study, we performed a comprehensive single-cell analysis of mouse border-associated macrophages using sequencing data obtained from the Gene Expression Omnibus(GEO) database(GSE174574 and GSE225948). Differentially expressed genes were identified, and enrichment analysis was performed to identify the transcription profile of border-associated macrophages. CellChat analysis was conducted to determine the cell communication network of border-associated macrophages. Transcription factors were predicted using the ‘pySCENIC' tool. We found that, in response to hypoxia, borderassociated macrophages underwent dynamic transcriptional changes and participated in the regulation of inflammatory-related pathways. Notably, the tumor necrosis factor pathway was activated by border-associated macrophages following ischemic stroke. The pySCENIC analysis indicated that the activity of signal transducer and activator of transcription 3(Stat3) was obviously upregulated in stroke, suggesting that Stat3 inhibition may be a promising strategy for treating border-associated macrophages-induced neuroinflammation. Finally, we constructed an animal model to investigate the effects of border-associated macrophages depletion following a stroke. Treatment with liposomes containing clodronate significantly reduced infarct volume in the animals and improved neurological scores compared with untreated animals. Taken together, our results demonstrate comprehensive changes in border-associated macrophages following a stroke, providing a theoretical basis for targeting border-associated macrophages-induced neuroinflammation in stroke treatment.展开更多
As large-scale astronomical surveys,such as the Sloan Digital Sky Survey(SDSS)and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST),generate increasingly complex datasets,clustering algorithms have...As large-scale astronomical surveys,such as the Sloan Digital Sky Survey(SDSS)and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST),generate increasingly complex datasets,clustering algorithms have become vital for identifying patterns and classifying celestial objects.This paper systematically investigates the application of five main categories of clustering techniques-partition-based,density-based,model-based,hierarchical,and“others”-across a range of astronomical research over the past decade.This review focuses on the six key application areas of stellar classification,galaxy structure analysis,detection of galactic and interstellar features,highenergy astrophysics,exoplanet studies,and anomaly detection.This paper provides an in-depth analysis of the performance and results of each method,considering their respective suitabilities for different data types.Additionally,it presents clustering algorithm selection strategies based on the characteristics of the spectroscopic data being analyzed.We highlight challenges such as handling large datasets,the need for more efficient computational tools,and the lack of labeled data.We also underscore the potential of unsupervised and semi-supervised clustering approaches to overcome these challenges,offering insight into their practical applications,performance,and results in astronomical research.展开更多
Effective control of gas-phase pollutants(volatile organic compounds(VOCs)and CO)is critical to human health and the ecological environment.Catalytic oxidation is one of the most promising technologies for achieving e...Effective control of gas-phase pollutants(volatile organic compounds(VOCs)and CO)is critical to human health and the ecological environment.Catalytic oxidation is one of the most promising technologies for achieving efficient volatile organic compounds and CO emission control.The subnano cluster catalyst can not only provide catalytic sites with multiple metal atoms,but also maintain full utilization efficiency.Almost all metal atoms in highly dispersed clusters can be used for adsorption and conversion of reactants.Recently,various types of sub-nano clusters,including subnano cluster oxides,have been developed and demonstrated excellent performance in low-temperature gas-phase pollutants combustion.In this mini review,we systematically summarize the structure,physicochemical properties,characterization,and applications of sub-nano cluster catalysts in catalytic oxidation of CO,methane,propane,propylene,toluene and its derivatives,formaldehyde and chlorinated volatile organic compounds.Finally,we have analyzed and discussed the problems and challenges faced by sub-nano cluster catalysts in both basic research and practical applications,providing a scientific basis for the design,synthesis,and application of efficient heterogeneous catalysts for CO and VOCs oxidation.展开更多
Background:Receptor-interacting protein kinases(RIPKs)regulate cell death,inflammation,and immune responses,yet their roles in cancer are not fully understood.This study investigates the expression,genomic alterations...Background:Receptor-interacting protein kinases(RIPKs)regulate cell death,inflammation,and immune responses,yet their roles in cancer are not fully understood.This study investigates the expression,genomic alterations,and functional implications of RIPK family members across various cancers.Methods:We collected multi-omics data from The Cancer Genome Atlas and other public databases,including gene expression,copy number variation(CNV),mutation,methylation,tumor mutation burden(TMB),and microsatellite instability(MSI).Differential expression and survival analyses were performed using DESeq2 and Cox proportional hazards models.CNV and mutation data were analyzed with GISTIC2 and Mutect2,and methylation data with the ChAMP package.Correlations with TMB and MSI were assessed using Pearson coefficients,and gene set enrichment analysis was conducted with the MSigDB Hallmark gene sets.Results:RIPK family members show significant differential expression in various cancers,with RIPK1 and RIPK4 frequently altered.Survival analysis reveals heterogeneous impacts on overall survival.CNV and mutation analyses identify high alteration frequencies for RIPK2 and RIPK7,affecting gene expression.RIPK1 and RIPK7 are hypermethylated in several cancers,inversely correlating with RIPK3 expression.RIPK1,RIPK2,RIPK5,RIPK6,and RIPK7 correlate positively with TMB,while RIPK3 shows negative correlations in some cancers.MSI analysis indicates associations with DNA mismatch repair.G ene set enrichment analysis highlights immune-related pathway enrichment for RIPK1,RIPK2,RIPK3,and RIPK6,and cell proliferation and DNA repair pathways for RIPK4 and RIPK5.RIPK family members showed heterogeneous alterations across cancers:for example,RIPK7 was mutated in up to~15%of u terine c orpus e ndometrial c arcinoma and l ung s quamous c ell c arcinoma cases,and RIPK1 and RIPK7 exhibited frequent promoter hypermethylation in multiple tumor types.Several genes displayed context-dependent associations with overall survival and with TMB/MSI.Conclusion:This pan-cancer analysis of the RIPK family reveals their diverse roles and potential as biomarkers and therapeutic targets.The findings emphasize the importance of RIPK genes in tumorigenesis and suggest context-dependent functions across cancer types.Further studies are needed to explore their mechanisms in cancer development and clinical applications.展开更多
In the effort to enhance cardiovascular diagnostics,deep learning-based heart sound classification presents a promising solution.This research introduces a novel preprocessing method:iterative k-means clustering combi...In the effort to enhance cardiovascular diagnostics,deep learning-based heart sound classification presents a promising solution.This research introduces a novel preprocessing method:iterative k-means clustering combined with silhouette score analysis,aimed at downsampling.This approach ensures optimal cluster formation and improves data quality for deep learning models.The process involves applying k-means clustering to the dataset,calculating the average silhouette score for each cluster,and selecting the clusterwith the highest score.We evaluated this method using 10-fold cross-validation across various transfer learningmodels fromdifferent families and architectures.The evaluation was conducted on four datasets:a binary dataset,an augmented binary dataset,amulticlass dataset,and an augmentedmulticlass dataset.All datasets were derived from the Heart Wave heart sounds dataset,a novelmulticlass dataset introduced by our research group.To increase dataset sizes and improve model training,data augmentation was performed using heartbeat cycle segmentation.Our findings highlight the significant impact of the proposed preprocessing approach on the HeartWave datasets.Across all datasets,model performance improved notably with the application of our method.In augmented multiclass classification,the MobileNetV2 model showed an average weighted F1-score improvement of 27.10%.In binary classification,ResNet50 demonstrated an average accuracy improvement of 8.70%,reaching 92.40%compared to its baseline performance.These results underscore the effectiveness of clustering with silhouette score analysis as a preprocessing step,significantly enhancing model accuracy and robustness.They also emphasize the critical role of preprocessing in addressing class imbalance and advancing precision medicine in cardiovascular diagnostics.展开更多
This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the compl...This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.展开更多
基金supported by the Specific Research Fund for Top-notch Talents of Guangdong Provincial Hospital of Chinese Medicine(No.2022KT1188).
文摘Objectives To identify core symptoms and symptom clusters in patients with neuromyelitis optica spectrum disorder(NMOSD)by network analysis.Methods From October 10 to 30,2023,140 patients with NMOSD were selected to participate in this online questionnaire survey.The survey tools included a general information questionnaire and a self-made NMOSD symptoms scale,which included the prevalence,severity,and distress of 29 symptoms.Cluster analysis was used to identify symptom clusters,and network analysis was used to analyze the symptom network and node characteristics and central indicators including strength centrality(r_(s)),closeness centrality(r_(c))and betweeness centrality(r_(b))were used to identify core symptoms and symptom clusters.Results The most common symptom was pain(65.7%),followed by paraesthesia(65.0%),fatigue(65.0%),easy awakening(63.6%).Regarding the burden level of symptoms,pain was the most burdensome symptom,followed by paraesthesia,easy awakening,fatigue,and difficulty falling asleep.Six clusters were identified:somatosensory,motor,visual,and memory symptom clusters,bladder and rectum symptom clusters,sleep symptoms clusters,and neuropsychological symptom clusters.Fatigue(r_(s)=12.39,r_(b)=68.00,r_(c)=0.02)was the most central and prominent bridge symptom,and motor symptom cluster(r_(s)=2.68,r_(c)=0.10)was the most central symptom cluster among the six clusters.Conclusions Our study demonstrated the necessity of symptom management targeting fatigue,pain,and motor symptom cluster in patients with NMOSD.
基金financed by the grants from the National Natural Science Foundation of China(No.81803996)Shanghai Key Laboratory of Health Identification and Assessment(No.21DZ2271000)。
文摘Traditional Chinese medicine(TCM)has played a significant role in the prevention and treatment of chronic heart failure(CHF).To study TCM diagnosis of CHF,a total of 278 Chinese clinical research articles on the study of CHF syndromes in recent 40 years retrieved from Web of Science,Scopus,Pub Med,Embase,CNKI,Wanfang Data,Cq VIP,and Sino Med.According to cumulative frequency analysis,network analysis,and hierarchical cluster analysis,the study found the distribution of CHF syndromes was syndrome of qi deficiency with blood stasis,syndrome of qi and yin deficiency,syndrome of yang deficiency with water flooding,syndrome of heart blood stasis obstruction,syndrome of turbid phlegm,and syndrome of collapse due to primordial yang deficiency.The syndrome elements on location of illness were heart,kidney,lung,and spleen.The syndrome elements on nature of illness were qi deficiency,blood stasis,yang deficiency,yin deficiency,water retention,and turbid phlegm.These findings can provide reference to the research on diagnosis and treatment of CHF,and contribute to the study on syndrome standardization and objective research of TCM diagnosis.
文摘Purpose:This study analyzes the profiles of elite Brazilian researchers,recognized through the prestigious CNPq productivity scholarships.By identifying distinct researcher clusters,the study sheds light on different academic strategies,levels of productivity,and academic contributions within the Brazilian higher education system.Design/methodology/approach:The research analyzes a comprehensive dataset of 14,003 researchers,employing principal component analysis(PCA)followed by cluster analysis to group researchers based on their academic attributes.The clusters reflect diverse aspects of research productivity,graduate supervisions,and publication patterns.Findings:The analysis reveals the existence of three distinct researcher profiles(the Advanced Supervisors,the Book Publishers/Organizers,and the Generalists).The study also highlights the characteristics of highcaliber scientists,representing the upper echelon of Brazilian researchers in terms of productivity and impact.Research limitations:Although the study provides a robust analysis of the Brazilian system,the results reflect specific characteristics of the Brazilian academic context.Furthermore,the analysis was restricted to normalized annual data,which may overlook temporal variations in researcher productivity.Pratical implications:The findings provide valuable insights for policy makers,funding agencies(such as CNPq),and university administrators who aim to develop tailored support programs for different researcher profiles.Originality/value:The cluster-based profiling offers a novel perspective on how different academic trajectories coexist within a national science system,offering lessons for other emerging economies.
基金State Grid Jiangsu Electric Power Co.,Ltd.Technology Project(J2023121).
文摘With the continuous expansion of the power system scale and the increasing complexity of operational mode,the interaction between transmission and distribution systems is becoming more and more significant,placing higher requirements on the accuracy and efficiency of the power system state estimation to address the challenge of balancing computational efficiency and estimation accuracy in traditional coupled transmission and distribution state estimation methods,this paper proposes a collaborative state estimation method based on distribution systems state clustering and load model parameter identification.To resolve the scalability issue of coupled transmission and distribution power systems,clustering is first carried out based on the distribution system states.As the data and models of the transmission system and distribution systems are not shared.For the transmission system,equating the power transmitted from the transmission system to the distribution system is the same as equating the distribution system.Further,the power transmitted from the transmission system to different types of distribution systems is equivalent to different polynomial equivalent load models.Then,a parameter identification method is proposed to obtain the parameters of the equivalent load model.Finally,a transmission and distribution collaborative state estimation model is constructed based on the equivalent load model.The results of the numerical analysis show that compared with the traditional master-slave splitting method,the proposed method significantly enhances computational efficiency while maintaining high estimation accuracy.
基金supported by the National Natural Science Foundation of China(42174003)the Gansu Provincial Department of Education:Innovation Fund Project for College Teachers(2023A-035)+1 种基金Gansu Provincial Science and Technology Program(Joint Research Fund),24JRRA856the Lanzhou Talent Innovation Project,2023-RC-31.
文摘Cycle slip detection and repair is one of the key technologies for GNSS high-precision positioning.We introduce an enhanced methodology for detecting and repairing BDS four-frequency cycle slips,utilizing fuzzy clustering analysis.Firstly,based on fuzzy clustering analysis,the optimal combinations for the BDS four-frequency,including extra-wide lane(EWL),wide lane(WL),and narrow lane(NL),were selected.Secondly,the feasibility of this method was verified using actual static and dynamic observation data,and different types of cycle slips were simulated for further validation.Meanwhile,the proposed method was compared with the classical Turbo-Edit method through experiments.Finally,cycle slips were repaired using the least squares method.According to the experimental results,the optimal geometry-free phase combinations(-2,2,1,-1),(1,-1,1,-1),(3,2,-2,-3),and the pseudo-range phase combination(-1,1,1,-1),selected based on fuzzy clustering analysis,were used for cycle slip detection.The proposed method accurately detected small,large,and specific cycle slips simulated in the actual data.Compared with the Turbo-Edit method,the proposed methodwas able to detect specific cycle slips that Turbo-Edit could not.It is worth noting that during the repair process,the coefficients of the combined observation values are integers,preserving the integer cycle characteristic of the observation values,which allows cycle slips to be fixed directly,eliminating the need for complex searching procedures.Consequently,by enhancing the precision and reliability of the detection of BDS four-frequency cycle slips,our proposed method provides the support for the high-precision localization of BDS multi-frequency observations.
基金supported by the National Key Research and Development Program Project of China(Grant No.2023YFF0718003)the key research and development plan project of Yunnan Province(Grant No.202303AA080006).
文摘The issue of strong noise has increasingly become a bottleneck restricting the precision and application space of electromagnetic exploration methods.Noise suppression and extraction of effective electromagnetic response information under a strong noise background is a crucial scientific task to be addressed.To solve the noise suppression problem of the controlled-source electromagnetic method in strong interference areas,we propose an approach based on complex-plane 2D k-means clustering for data processing.Based on the stability of the controlled-source signal response,clustering analysis is applied to classify the spectra of different sources and noises in multiple time segments.By identifying the power spectra with controlled-source characteristics,it helps to improve the quality of the controlled-source response extraction.This paper presents the principle and workflow of the proposed algorithm,and demonstrates feasibility and effectiveness of the new algorithm through synthetic and real data examples.The results show that,compared with the conventional Robust denoising method,the clustering algorithm has a stronger suppression effect on common noise,can identify high-quality signals,and improve the preprocessing data quality of the controlledsource electromagnetic method.
文摘Side-channel analysis(SCA)has emerged as a research hotspot in the field of cryptanalysis.Among various approaches,unsupervised deep learning-based methods demonstrate powerful information extraction capabilities without requiring labeled data.However,existing unsupervised methods,particularly those represented by differential deep learning analysis(DDLA)and its improved variants,while overcoming the dependency on labeled data inherent in template analysis,still suffer from high time complexity and training costs when handling key byte difference comparisons.To address this issue,this paper introduces invariant information clustering(IIC)into SCA for the first time,and thus proposes a novel unsupervised learning-based SCA method,named IIC-SCA.By leveraging mutual information maximization techniques for automatic feature extraction of power leakage data,our approach achieves key recovery through a single training session,eliminating the prohibitive computational overhead of traditional methods that require separate training for all possible key bytes.Experimental results on the ASCAD dataset demonstrate successful key extraction using only 50000 training traces and 2000 attack traces.Furthermore,compared with DDLA,the proposed method reduces training time by approximately 93.40%and memory consumption by about 6.15%,significantly decreasing the temporal and resource costs of unsupervised SCA.This breakthrough provides new insights for developing low-cost,high-efficiency cryptographic attack methodologies.
文摘Objective:This study aims to investigate the patterns of symptom occurrence in patients experiencing acute exacerbations of chronic obstructive pulmonary disease(AECOPD).It will explore the composition of symptom clusters and analyze the correlation between these clusters and health-related quality of life(HRQoL).Methods:A total of 207 patients with AE-COPD were surveyed from a tertiary grade A hospital.Data collection was conducted using three validated instruments:the Basic Information Questionnaire(BIQ),Disease Symptom Survey Questionnaire(MSAS),and Quality of Life Questionnaire(CAT).Statistical software SPSS 22.0 was used to analyze the correlation between symptom clusters and quality of life.Results:Exploratory factor analysis showed that five major symptom clusters existed in the patients,including the psycho-emotional symptom cluster,the sleep-related symptom cluster,the other side effects symptom cluster,the energy deficiency symptom cluster and the cough-loss of appetite symptom cluster,and the severity of the symptom clusters showed a significant negative correlation with the quality of life of the patients(P<0.05).Conclusion:Strengthening the comprehensive management of symptom clusters in patients with AE-COPD can help to effectively reduce the symptom burden of patients,and then significantly improve their quality of life.
基金supported by the CCF-NSFOCUS‘Kunpeng’Research Fund(CCF-NSFOCUS2024012).
文摘In recent years,with the rapid development of software systems,the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics.Defect prediction methods based on software metric elements highly rely on software metric data.However,redundant software metric data is not conducive to efficient defect prediction,posing severe challenges to current software defect prediction tasks.To address these issues,this paper focuses on the rational clustering of software metric data.Firstly,multiple software projects are evaluated to determine the preset number of clusters for software metrics,and various clustering methods are employed to cluster the metric elements.Subsequently,a co-occurrence matrix is designed to comprehensively quantify the number of times that metrics appear in the same category.Based on the comprehensive results,the software metric data are divided into two semantic views containing different metrics,thereby analyzing the semantic information behind the software metrics.On this basis,this paper also conducts an in-depth analysis of the impact of different semantic view of metrics on defect prediction results,as well as the performance of various classification models under these semantic views.Experiments show that the joint use of the two semantic views can significantly improve the performance of models in software defect prediction,providing a new understanding and approach at the semantic view level for defect prediction research based on software metrics.
文摘Under hydrothermal and solvothermal conditions,two novel cobalt-based complexes,{[Co_(2)(CIA)(OH)(1,4-dtb)]·3.2H_(2)O}n(HU23)and{[Co_(2)(CIA)(OH)(1,4-dib)]·3.5H2O·DMF}n(HU24),were successfully constructed by coordinatively assembling the semi-rigid multidentate ligand 5-(1-carboxyethoxy)isophthalic acid(H₃CIA)with the Nheterocyclic ligands 1,4-di(4H-1,2,4-triazol-4-yl)benzene(1,4-dtb)and 1,4-di(1H-imidazol-1-yl)benzene(1,4-dib),respectively,around Co^(2+)ions.Single-crystal X-ray diffraction analysis revealed that in both complexes HU23 and HU24,the CIA^(3-)anions adopt aκ^(7)-coordination mode,bridging six Co^(2+)ions via their five carboxylate oxygen atoms and one ether oxygen atom.This linkage forms tetranuclear[Co4(μ3-OH)2]^(6+)units.These Co-oxo cluster units were interconnected by CIA^(3-)anions to assemble into 2D kgd-type structures featuring a 3,6-connected topology.The 2D layers were further connected by 1,4-dtb and 1,4-dib,resulting in 3D pillar-layered frameworks for HU23 and HU24.Notably,despite the similar configurations of 1,4-dtb and 1,4-dib,differences in their coordination spatial orientations lead to topological divergence in the 3D frameworks of HU23 and HU24.Topological analysis indicates that the frameworks of HU23 and HU24 can be simplified into a 3,10-connected net(point symbol:(4^(10).6^(3).8^(2))(4^(3))_(2))and a 3,8-connected tfz-d net(point symbol:(4^(3))_(2)((4^(6).6^(18).8^(4)))),respectively.This structural differentiation confirms the precise regulatory role of ligands on the topology of metal-organic frameworks.Moreover,the ultraviolet-visible absorption spectra confirmed that HU23 and HU24 have strong absorption capabilities for ultraviolet and visible light.According to the Kubelka-Munk method,their bandwidths were 2.15 and 2.08 eV,respectively,which are consistent with those of typical semiconductor materials.Variable-temperature magnetic susceptibility measurements(2-300 K)revealed significant antiferromagnetic coupling in both complexes,with their effective magnetic moments decreasing markedly as the temperature lowered.CCDC:2457554,HU23;2457553,HU24.
文摘Six new lanthanide complexes:[Ln(3,4-DEOBA)3(4,4'-DM-2,2'-bipy)]2·2C_(2)H_(5)OH,[Ln=Dy(1),Eu(2),Tb(3),Sm(4),Ho(5),Gd(6);3,4-DEOBA-=3,4-diethoxybenzoate,4,4'-DM-2,2'-bipy=4,4'-dimethyl-2,2'-bipyridine]were successfully synthesized by the volatilization of the solution at room temperature.The crystal structures of six complexes were determined by single-crystal X-ray diffraction technology.The results showed that the complexes all have a binuclear structure,and the structures contain free ethanol molecules.Moreover,the coordination number of the central metal of each structural unit is eight.Adjacent structural units interact with each other through hydrogen bonds and further expand to form 1D chain-like and 2D planar structures.After conducting a systematic study on the luminescence properties of complexes 1-4,their emission and excitation spectra were obtained.Experimental results indicated that the fluorescence lifetimes of complexes 2 and 3 were 0.807 and 0.845 ms,respectively.The emission spectral data of complexes 1-4 were imported into the CIE chromaticity coordinate system,and their corre sponding luminescent regions cover the yellow light,red light,green light,and orange-red light bands,respectively.Within the temperature range of 299.15-1300 K,the thermal decomposition processes of the six complexes were comprehensively analyzed by using TG-DSC/FTIR/MS technology.The hypothesis of the gradual loss of ligand groups during the decomposition process was verified by detecting the escaped gas,3D infrared spectroscopy,and ion fragment information detected by mass spectrometry.The specific decomposition path is as follows:firstly,free ethanol molecules and neutral ligands are removed,and finally,acidic ligands are released;the final product is the corresponding metal oxide.CCDC:2430420,1;2430422,2;2430419,3;2430424,4;2430421,5;2430423,6.
基金Supported by National Natural Science Foundation of China,No.32270768,No.82273970,No.32070726,and No.82370715National Key R&D Program of China,No.2023YFC2507904the Innovation Group Project of Hubei Province,No.2023AFA026.
文摘Hepatitis B virus remains a major cause of cirrhosis and hepatocellular carcinoma,with genetic polymorphisms and mutations influencing immune responses and disease progression.Nguyen et al present novel findings on specific human leukocyte antigen(HLA)alleles,including rs2856718 of HLA-DQ and rs3077 and rs9277535 of HLA-DP,which may predispose individuals to cirrhosis and liver cancer,based on multi-clustering analysis.Here,we discuss the feasibility of this approach and identify key areas for further investigation,aiming to offer insights for advancing clinical practice and research in liver disease and related cancers.
文摘Objective Sepsis patients exhibit diverse immune states,making it crucial to identify subtypes with distinct inflammatory profiles through Th1/Th2 cytokine data for personalized treatment and improved prognosis.Methods We retrieved data from sepsis patients who underwent Th1/Th2 cytokine testing in Nanfang Hospital,Southern Medical University from June 1,2020,to February 1,2022.An unsupervised K-means clustering method classified participants based on Th1/Th2 cytokine levels,with the primary outcome being the 7-day mortality rate post-ICU admission.Cox proportional hazards and Restricted Mean Survival Time(RMST)analyses were utilized to explore survival outcomes.Results A total of 321 sepsis patients were included.IL-6(HR 1.69,95%CI:1.22,2.34)and IL-10(HR 1.81,95%CI:1.37,2.40)emerged as independent predictors of 7-day mortality.Unsupervised K-means clustering revealed 3 inflammatory/immune subgroups:Cluster 1(n=166,low inflammatory response),Cluster 2(n=99,moderate inflammatory response with immune suppression),and Cluster 3(n=56,strong inflammatory and immune suppression).Compared to Cluster 1,Clusters 2 and 3 had higher 7-day mortality risks(14.4%vs 23.2%,HR=4.30,95%CI:1.51-12.26;14.4%vs 35.7%,HR=7.32,95%CI:2.57-20.79).Conclusion Septic patients in a protective immune response state(Cluster 1)exhibit better short-term prognoses,suggesting the importance of understanding inflammatory/immune states for precise treatment and improved outcomes.
基金supported by Qingdao Key Medical and Health Discipline ProjectThe Intramural Research Program of the Affiliated Hospital of Qingdao University,No. 4910Qingdao West Coast New Area Science and Technology Project,No. 2020-55 (all to SW)。
文摘Border-associated macrophages are located at the interface between the brain and the periphery, including the perivascular spaces, choroid plexus, and meninges. Until recently, the functions of border-associated macrophages have been poorly understood and largely overlooked. However, a recent study reported that border-associated macrophages participate in stroke-induced inflammation, although many details and the underlying mechanisms remain unclear. In this study, we performed a comprehensive single-cell analysis of mouse border-associated macrophages using sequencing data obtained from the Gene Expression Omnibus(GEO) database(GSE174574 and GSE225948). Differentially expressed genes were identified, and enrichment analysis was performed to identify the transcription profile of border-associated macrophages. CellChat analysis was conducted to determine the cell communication network of border-associated macrophages. Transcription factors were predicted using the ‘pySCENIC' tool. We found that, in response to hypoxia, borderassociated macrophages underwent dynamic transcriptional changes and participated in the regulation of inflammatory-related pathways. Notably, the tumor necrosis factor pathway was activated by border-associated macrophages following ischemic stroke. The pySCENIC analysis indicated that the activity of signal transducer and activator of transcription 3(Stat3) was obviously upregulated in stroke, suggesting that Stat3 inhibition may be a promising strategy for treating border-associated macrophages-induced neuroinflammation. Finally, we constructed an animal model to investigate the effects of border-associated macrophages depletion following a stroke. Treatment with liposomes containing clodronate significantly reduced infarct volume in the animals and improved neurological scores compared with untreated animals. Taken together, our results demonstrate comprehensive changes in border-associated macrophages following a stroke, providing a theoretical basis for targeting border-associated macrophages-induced neuroinflammation in stroke treatment.
文摘As large-scale astronomical surveys,such as the Sloan Digital Sky Survey(SDSS)and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope(LAMOST),generate increasingly complex datasets,clustering algorithms have become vital for identifying patterns and classifying celestial objects.This paper systematically investigates the application of five main categories of clustering techniques-partition-based,density-based,model-based,hierarchical,and“others”-across a range of astronomical research over the past decade.This review focuses on the six key application areas of stellar classification,galaxy structure analysis,detection of galactic and interstellar features,highenergy astrophysics,exoplanet studies,and anomaly detection.This paper provides an in-depth analysis of the performance and results of each method,considering their respective suitabilities for different data types.Additionally,it presents clustering algorithm selection strategies based on the characteristics of the spectroscopic data being analyzed.We highlight challenges such as handling large datasets,the need for more efficient computational tools,and the lack of labeled data.We also underscore the potential of unsupervised and semi-supervised clustering approaches to overcome these challenges,offering insight into their practical applications,performance,and results in astronomical research.
基金supported by the National Natural Science Foundation of China(No.22506042)the Natural Science Foundation of Henan Province(Nos.252300421710 and 252300421552)the High level Talent Research Launch Fund of Henan University of Technology(No.2024BS061).
文摘Effective control of gas-phase pollutants(volatile organic compounds(VOCs)and CO)is critical to human health and the ecological environment.Catalytic oxidation is one of the most promising technologies for achieving efficient volatile organic compounds and CO emission control.The subnano cluster catalyst can not only provide catalytic sites with multiple metal atoms,but also maintain full utilization efficiency.Almost all metal atoms in highly dispersed clusters can be used for adsorption and conversion of reactants.Recently,various types of sub-nano clusters,including subnano cluster oxides,have been developed and demonstrated excellent performance in low-temperature gas-phase pollutants combustion.In this mini review,we systematically summarize the structure,physicochemical properties,characterization,and applications of sub-nano cluster catalysts in catalytic oxidation of CO,methane,propane,propylene,toluene and its derivatives,formaldehyde and chlorinated volatile organic compounds.Finally,we have analyzed and discussed the problems and challenges faced by sub-nano cluster catalysts in both basic research and practical applications,providing a scientific basis for the design,synthesis,and application of efficient heterogeneous catalysts for CO and VOCs oxidation.
基金supported by grants from the Tianjin Health Technology Project(Grant no.2022QN106).
文摘Background:Receptor-interacting protein kinases(RIPKs)regulate cell death,inflammation,and immune responses,yet their roles in cancer are not fully understood.This study investigates the expression,genomic alterations,and functional implications of RIPK family members across various cancers.Methods:We collected multi-omics data from The Cancer Genome Atlas and other public databases,including gene expression,copy number variation(CNV),mutation,methylation,tumor mutation burden(TMB),and microsatellite instability(MSI).Differential expression and survival analyses were performed using DESeq2 and Cox proportional hazards models.CNV and mutation data were analyzed with GISTIC2 and Mutect2,and methylation data with the ChAMP package.Correlations with TMB and MSI were assessed using Pearson coefficients,and gene set enrichment analysis was conducted with the MSigDB Hallmark gene sets.Results:RIPK family members show significant differential expression in various cancers,with RIPK1 and RIPK4 frequently altered.Survival analysis reveals heterogeneous impacts on overall survival.CNV and mutation analyses identify high alteration frequencies for RIPK2 and RIPK7,affecting gene expression.RIPK1 and RIPK7 are hypermethylated in several cancers,inversely correlating with RIPK3 expression.RIPK1,RIPK2,RIPK5,RIPK6,and RIPK7 correlate positively with TMB,while RIPK3 shows negative correlations in some cancers.MSI analysis indicates associations with DNA mismatch repair.G ene set enrichment analysis highlights immune-related pathway enrichment for RIPK1,RIPK2,RIPK3,and RIPK6,and cell proliferation and DNA repair pathways for RIPK4 and RIPK5.RIPK family members showed heterogeneous alterations across cancers:for example,RIPK7 was mutated in up to~15%of u terine c orpus e ndometrial c arcinoma and l ung s quamous c ell c arcinoma cases,and RIPK1 and RIPK7 exhibited frequent promoter hypermethylation in multiple tumor types.Several genes displayed context-dependent associations with overall survival and with TMB/MSI.Conclusion:This pan-cancer analysis of the RIPK family reveals their diverse roles and potential as biomarkers and therapeutic targets.The findings emphasize the importance of RIPK genes in tumorigenesis and suggest context-dependent functions across cancer types.Further studies are needed to explore their mechanisms in cancer development and clinical applications.
基金supported by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under grant No.IPP:533-611-2025DSR technical and financial support.
文摘In the effort to enhance cardiovascular diagnostics,deep learning-based heart sound classification presents a promising solution.This research introduces a novel preprocessing method:iterative k-means clustering combined with silhouette score analysis,aimed at downsampling.This approach ensures optimal cluster formation and improves data quality for deep learning models.The process involves applying k-means clustering to the dataset,calculating the average silhouette score for each cluster,and selecting the clusterwith the highest score.We evaluated this method using 10-fold cross-validation across various transfer learningmodels fromdifferent families and architectures.The evaluation was conducted on four datasets:a binary dataset,an augmented binary dataset,amulticlass dataset,and an augmentedmulticlass dataset.All datasets were derived from the Heart Wave heart sounds dataset,a novelmulticlass dataset introduced by our research group.To increase dataset sizes and improve model training,data augmentation was performed using heartbeat cycle segmentation.Our findings highlight the significant impact of the proposed preprocessing approach on the HeartWave datasets.Across all datasets,model performance improved notably with the application of our method.In augmented multiclass classification,the MobileNetV2 model showed an average weighted F1-score improvement of 27.10%.In binary classification,ResNet50 demonstrated an average accuracy improvement of 8.70%,reaching 92.40%compared to its baseline performance.These results underscore the effectiveness of clustering with silhouette score analysis as a preprocessing step,significantly enhancing model accuracy and robustness.They also emphasize the critical role of preprocessing in addressing class imbalance and advancing precision medicine in cardiovascular diagnostics.
基金supported by the Research Project of China Southern Power Grid(No.056200KK52222031).
文摘This paper proposes an equivalent modeling method for photovoltaic(PV)power stations via a particle swarm optimization(PSO)K-means clustering(KMC)algorithm with passive filter parameter clustering to address the complexities,simulation time cost and convergence problems of detailed PV power station models.First,the amplitude–frequency curves of different filter parameters are analyzed.Based on the results,a grouping parameter set for characterizing the external filter characteristics is established.These parameters are further defined as clustering parameters.A single PV inverter model is then established as a prerequisite foundation.The proposed equivalent method combines the global search capability of PSO with the rapid convergence of KMC,effectively overcoming the tendency of KMC to become trapped in local optima.This approach enhances both clustering accuracy and numerical stability when determining equivalence for PV inverter units.Using the proposed clustering method,both a detailed PV power station model and an equivalent model are developed and compared.Simulation and hardwarein-loop(HIL)results based on the equivalent model verify that the equivalent method accurately represents the dynamic characteristics of PVpower stations and adapts well to different operating conditions.The proposed equivalent modeling method provides an effective analysis tool for future renewable energy integration research.
